Bed Human

In-bed human pose estimation aims to accurately determine a person's body position while they are lying in bed, using various imaging modalities like RGB, infrared, depth, and pressure sensors. Research focuses on developing robust algorithms, often employing deep learning architectures like convolutional neural networks and variational autoencoders, to overcome challenges such as occlusions from bedding and limited labeled data. This technology holds significant promise for improving sleep monitoring, early disease detection, and patient care, particularly through the development of privacy-preserving methods using non-visual data.